What is a Pragmatic Clinical Trial
Why Are We Talking About Pragmatic Trials?
Karen Staman, MS
Jonathan McCall, MS
Liz Wing, MA
Healthcare in the United States is increasingly complex and expensive, and there is a great need for more evidence to inform decisions that lead to improved, efficient, and affordable care (Alper and Grossmann 2015). Care providers, researchers, administrators, payers, regulators, and the public agree that the provision of medical care should be informed by high-quality scientific evidence regarding the risks and benefits of treatments. Yet, this high-quality evidence—generated by conducting randomized controlled trials (RCTs) and disseminated through clinical practice guidelines—is severely lacking across a multitude of specialties (Tricoci et al. 2009; Roos et al. 2011; Wright et al. 2011; Koh et al. 2013; Feuerstein et al. 2014; Neuman et al. 2014). In the absence of this evidence, clinicians must make educated guesses to determine treatment based on personal judgment and knowledge of the patient, rather than on the consensus of a group of clinical experts (Tricoci et al. 2009). Clinicians and patients simply do not have enough evidence to effectively inform clinical decisions. For example, in the field of cardiology, which arguably has one of the most robust evidence bases among specialties, the majority of treatment recommendations are founded upon lower-quality trials, observational studies, or expert opinion (Tricoci et al. 2009).
In addition, when we survey the US clinical trials enterprise from a broad perspective, the kind of trials needed to provide medical evidence to support treatment decisions are, for the most part, not being done. Analyses of trials contained in the ClinicalTrials.gov database have shown that the vast majority of clinical trials are too small to provide sufficient statistical power to definitively answer clinical questions, they fail to address critical treatment priorities, or they suffer from shortcomings in design and execution that limit their usefulness (Califf et al. 2012; Pasquali et al. 2012; Alexander et al. 2013; Goswami et al. 2013; Hirsch et al. 2013; Lakey et al. 2013; Todd et al. 2013; Witsell et al. 2013; Inrig et al. 2014; Subherwal et al. 2014). In addition, the data from many of these trials are not being reported in timely and transparent ways (Anderson et al. 2015). Adding to these complications, there has been a steady drumbeat of revelations indicating that many findings published in the peer-reviewed literature are fundamentally unreliable (Ioannidis 2005; Ioannidis 2016; Open Science Collaboration 2015; Le Noury et al. 2015).
For much of modern medical history, clinical research has been kept separate from the delivery of routine patient care, resulting in an environment in which research data are collected using standalone systems. These systems are designed to ensure that the information gathered during research activities is valid and complete. However, having separate systems for research and care comes at a significant cost. There is growing concern that the results obtained from clinical research may not apply to “real-world” situations (Ioannidis 2005), because the research is often done under artificial conditions with volunteers who may not reflect the patients who actually live with a given disease or condition. Further, standalone systems require enormous amounts of money and effort to sustain.
Learning Health Systems and Embedded Clinical Research
Many are now advocating a move to a learning health system in which tools such as computing power, connectivity, team-based care, and systems engineering techniques will produce a culture of continuous learning at lower cost (Institute of Medicine 2013). Ideally, clinical trials would be embedded within a system of healthcare delivery where evidence is rapidly and continually fed back into clinical care, and clinical care itself would inform the further development of medical evidence (Embi 2019; Simon 2020). At the same time, the widespread use of electronic health records and advances in information technology and informatics are creating opportunities to combine very large, complex sets of data (“big data”) in ways that until now were almost unimaginable. As systems for managing data continue to improve within US health systems, the availability of electronic data is likewise improving rapidly.
There is a need for “a different context to clinical research that could speed the discovery and implementation of evidence-based advancements to healthcare delivery. Pragmatic clinical trials (PCTs) are a promising type of trial conducted within real-world health care delivery systems” (Tuzzio and Larson 2019).
The emergence of PCTs embedded in healthcare systems, or ePCTs, represents one approach that could support a goal of a learning health system by informing real-world practice with digital health data collected at the point of care. ePCTs have the potential to inform policy and practice with high-quality evidence at reduced cost and increased efficiency compared with traditional clinical trials.
The next sections introduce characteristics of ePCTs, with examples drawn from the NIH Collaboratory Demonstration Projects, and point readers to rich resources available in this Living Textbook that describe best practices around how to design, conduct, and disseminate ePCTs.
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